Lead Data Engineer

Mobikwik is Indias leading Digital Financial Services platform & Largest Digital Wallet, trusted by over 167 million users & 4.4million businesses. As a pioneer in the Fintech space, Mobikwik empowers its users with a robust suite of services, including

Digital payments, Credit & Investments. Recently, Mobikwik achieved a significant milestone with its Initial Public Offering

(IPO), marking a new chapter in its journey of innovation & growth. With a mission to democratize financial inclusion, Mobikwik continues to transform how Indians manage their money, offering secure, seamless & user-friendly solutions that cater to a diverse range of financial needs. Join us on our journey to democratize access to financial services for a billion Indians.

Job Title

Experience

Location

Role Overview

Lead Data Engineer

Key Responsibilities

• Lead the design, development, and optimization of scalable and reliable

  ETL/data pipelines

  for high-volume data.
• Build and manage workflows using

  Apache Airflow

  for scheduling and orchestration.
• Architect and maintain

  data lake and data warehouse solutions

  on

  AWS

  (S3, Redshift, EMR, Glue, Lambda, etc.).
• Develop data models and frameworks to ensure availability, scalability, and quality of data.
• Collaborate with product, analytics, and data science teams to provide clean, curated datasets for decision-making and modeling.
• Implement best practices in

  data governance, security, monitoring, and performance optimization

  .
• Mentor junior data engineers and guide the team on adopting modern data engineering practices.

Requirements

• 6+ years

  of experience in data engineering, with at least

  2+ years in a lead/mentorship role

  .
• Strong hands-on expertise in

  PySpark (or Spark with Python)

  for large-scale data processing.
• Proven experience with

  Apache Airflow

  for job orchestration and scheduling.
• Deep knowledge of

  AWS cloud ecosystem

  (S3, Redshift, Glue, EMR, Lambda, Athena, etc.).
• Strong background in building and scaling

  ETL/data pipelines

  for structured and unstructured data.
• Solid understanding of

  data modeling, warehousing concepts, and big data frameworks

  .
• Proficiency in

  SQL

  and performance optimization for large datasets.
• Familiarity with CI/CD practices, version control (Git), and DevOps for data workflows.
• Excellent problem-solving skills, leadership ability, and communication skills.

Good to Have

• Experience with

  streaming platforms

  (Kafka, Kinesis, Flink).
• Exposure to

  machine learning pipelines

  and MLOps.
• Familiarity with containerization and orchestration (Docker, Kubernetes).